Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact
This addresses alarm fatigue in healthcare personnel, improving patient safety, but is incremental as it applies weak supervision to an existing problem.
The paper tackled the problem of high false alarm rates in clinical monitoring by developing a weakly supervised model to classify vital sign alerts as real or artifact, achieving competitive performance with traditional supervised methods while reducing the need for hand-labeled data.
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact. Previous studies have utilized supervised ML techniques that require substantial amounts of hand-labeled data. However, manually harvesting such data can be costly, time-consuming, and mundane, and is a key factor limiting the widespread adoption of ML in healthcare (HC). Instead, we explore the use of multiple, individually imperfect heuristics to automatically assign probabilistic labels to unlabeled training data using weak supervision. Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.